This article describes the methods that the AUEB NLP Group experimented with during its participation in the 7th edition of the ImageCLEFmedical Caption sub- ...

This article describes the methods that the AUEB NLP Group experimented with during its participation in the 7th edition of the ImageCLEFmedical Caption sub- ...

ranked 1st in the Concept Detection and 3rd in the Caption Prediction sub-tasks, according to the competition's primary evaluation metrics. - Our best ...

ImageCLEFmedical Caption 2023 consists of two substaks: Concept Detection ... AUEB-NLP-Group, 2, 0.617034, 0.213014, 0.295011, 0.169212, 0.071982, 0.146601 ...

Oct 5, 2023 · The ImageCLEFmedical 2023 Caption task on caption prediction and concept detection follows similar challenges held from 2017–2022.

Sep 21, 2023 · AUEB-NLP-Group The AUEB-NLP-Group reached a BERTScore of 0.6170 [11] and a ROUGE score of 0.2130, placing third. Their best approach is a novel ...

For ImageCLEF 2023, several issues with the dataset (large number of concepts, lemmatization errors, duplicate captions) were tackled and based on ...

This article describes the methods that the AUEB NLP Group experimented with during its participation in the 7th edition of the ImageCLEFmedical Caption ...

Sep 18, 2023 · Participants mainly used multi-label classification systems for the concept detection subtask, the winning team AUEB-NLP-Group used an ensemble ...

Participation of the AUEB NLP Group in the 8th edition of the ImageCLEFmedical Caption evaluation campaign. nlpaueb/imageclef2024's past year of commit activity.